W5: Iterating tasks

Iterating tasks

Suppose that you want to repeat a chunk of code many times, but changing one variable’s value each time you do it: This could be modifying each element of a vector in the same way, or analyzing a dataframe multiple times with different parameters.

Review of lists

• Remember, lists are the most general data structure

Functionals via map()

map() takes in a vector or a list, and then applies the function on each element of it. The output is always a list.

map(my_vector, log)
    ^vector   ^function

purrr::map()

Another View of map()

1. Loading up a list of files

2. Do it once

We want to load up four data frames in the data/tumor/ directory using read_csv. Let’s try doing it for one first:

3. Make a list

Now we build our list by listing the files in data/tumor/:

4. Do it multiple times

Now we can apply read_csv to each element in file_list. We can load these up by applying read_csv on each element of the file list.

Check what df_list[[1]] is:

1. Define What we want to do

2. Write a function, try it out on one DF

Write a function called plot_recurrence that plots days_to_last_follow_up vs. age_at_diagnosis. Try running it on lusc_data

plot_recurrence <- function(df){
  ggplot(df) +
    aes(x= days_to_last_followup,
        y= age_at_diagnosis) +
    geom_point()
}

plot_recurrence(lusc_data)

3. Build our list

• We use read_csv on our list of file paths.

4. Apply our Function to the list

Try applying plot_recurrence() to each element of df_list using map().

Critical Things to Think about:

• What is the unit in the list?
• How do I call a function?
• How do I call extra arguments?

Variations of map()

To be more specific about the output type, you can do this via the map_* function, where * specifies the output type: map_lgl(), map_chr(), and map_dbl() functions return vectors of logical values, strings, or numbers respectively.

map_dbl

Expects a single double return value:

map_lgl

Expects logical output from each element (TRUE, FALSE):

Case study 3: Iterate over different conditions to analyze a dataframe

Suppose you are working with the penguins dataframe:

head(penguins)

Step 1: Define what you want to do

We want to look at the mean bill_length_mm for each of the three species (Adelie, Chinstrap, Gentoo).

Step 2: Do it Once

Adapt the below code into a function. Try it out on the first element of species_to_analyze()

analyze_bill <- function(species_to_analyze){
  penguins_subset = filter(penguins, species == "Adelie")
  out <- mean(penguins_subset$bill_length_mm, na.rm = TRUE)
  return(out)
}

analyze_bill("Adelie")

Step 3: Make a list

• Variable we need to loop through: c("Adelie", "Chinstrap", "Gentoo")

Step 4: Apply function on list elements

• The looping mechanism, and its output: map_dbl() outputs (double) numeric vectors.

Apply analyze_bill to species_to_analyze:

map_dbl(-------, analyze_bill)

Map family of functions

More info at: https://adv-r.hadley.nz/functionals.html

For loops, briefly

A for loop repeats a chunk of code many times, once for each element of an input vector.

for (my_element in my_vector) {
  chunk of code
}

Most often, the “chunk of code” will make use of my_element.

my_vector = c(1, 3, 5, 7)
for (my_element in my_vector) {
  print(my_element)
}

[1] 1
[1] 3
[1] 5
[1] 7

Loop through the indicies of a vector

my_vector = c(1, 3, 5, 7)
seq_along(my_vector)

[1] 1 2 3 4

for(i in seq_along(my_vector)) {
  print(my_vector[i])
}

[1] 1
[1] 3
[1] 5
[1] 7

my_vector = c(1, 3, 5, 7)
result = rep(NA, length(my_vector))

for(i in seq_along(my_vector)) {
  result[i] = log(my_vector[i])
}

result

[1] 0.000000 1.098612 1.609438 1.945910